(2) Caltech Dataset. We also test our method on Caltech and the comparison with state-of-the-arts on this benchmark is shown in Table REF . Our method achieves MR\(^{-2}\)  of 3.08% under the IoU threshold of 0.5, which is comparable to the best competitor (3.80% of CSP [1]}). Besides, in the case of a stricter occlusion level of Heavy subset, our method achieves 30.45% MR\(^{-2}\) , outperforming all previous state-of-the-arts with an improvement of 6.05% MR\(^{-2}\)  over CSP [1]}. It indicates that our method has a substantially better localization accuracy.
